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Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed that defect category sensitive prediction models are more successful than general models, since each category has different characteristics in terms of metrics. We extend our previous work, and propose specialized prediction models using churn, code, and network metrics with respect to three defect categories. Results show that churn metrics are the best for predicting all defects. The strength of correlation for code and network metrics varies with defect category: Network metrics have higher correlations than code metrics for defects reported during functional testing and in the field, and vice versa for defects reported during system testing. [ABSTRACT FROM AUTHOR]

Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed that defect category sensitive prediction models are more successful than general models, since each category has different characteristics in terms of metrics. We extend our previous work, and propose specialized prediction models using churn, code, and network metrics with respect to three defect categories. Results show that churn metrics are the best for predicting all defects. The strength of correlation for code and network metrics varies with defect category: Network metrics have higher correlations than code metrics for defects reported during functional testing and in the field, and vice versa for defects reported during system testing. [ABSTRACT FROM AUTHOR]